My RAG Feature Pipeline Started Simple… Then Got Personal 🤖📦

I built a RAG feature pipeline thinking it would be clean:

“Just take raw data, process it, generate embeddings, store in vector DB… done.”

Yes.

“Done.”

Step 1: Clean the Data (aka emotional damage)

I opened my dataset.

It had:

broken text
random HTML
sentences that started in 2012 and ended in 2026

So I cleaned it.

Then cleaned it again.

Then realized:

“Cleaning data is just debugging… but slower.”

Step 2: Chunking (aka cutting things you don’t understand)

Now I had to split text into chunks.

Too big → model confused
Too small → model useless

So I picked a size and said:

“Looks reasonable.”

(It wasn’t.)

Step 3: Embeddings (aka turning words into math magic)

I converted text into vectors.

Thousands of them.

They looked like:

[0.123, -0.928, 0.44, …]

I nodded like I understood.

I did not.

Step 4: Store in Vector DB

Everything went into the database.

Fast. Scalable. Beautiful.

Until I queried it.

I asked:

“Find relevant context.”

It returned:

Something… technically related.

Emotionally unrelated.

Final Lesson

A RAG pipeline is not:

just cleaning
just chunking
just embedding

It’s:

making sure your future self doesn’t question your life choices.

Truth

If your RAG output is bad…

It’s not the model.

It’s your pipeline.

And that’s when I realized:

I didn’t build a feature pipeline.

I built a system that politely reflects my bad decisions… at scale.